1. Field of the Invention
The present invention generally relates to parallel computing. More specifically, the present invention relates to normalizing databases across compute nodes on a parallel computing system.
2. Description of the Related Art
Powerful computers may be designed as highly parallel systems where the processing activity of hundreds, if not thousands, of processors (CPUs) are coordinated to perform computing tasks. These systems are highly useful for a broad variety of applications including, financial modeling, hydrodynamics, quantum chemistry, astronomy, weather modeling and prediction, geological modeling, prime number factoring, image processing (e.g., CGI animations and rendering), to name but a few examples.
For example, one family of parallel computing systems has been (and continues to be) developed by International Business Machines (IBM) under the name Blue Gene®. The Blue Gene®/L architecture provides a scalable, parallel computer that may be configured with a maximum of 65,536 (216) compute nodes. Each compute node includes a single application specific integrated circuit (ASIC) with 2 CPU's and memory. The Blue Gene®/L architecture has been successful and on Oct. 27, 2005, IBM announced that a Blue Gene®/L system had reached an operational speed of 280.6 teraflops (280.6 trillion floating-point operations per second), making it the fastest computer in the world at that time. Further, as of June 2005, Blue Gene®/L installations at various sites world-wide were among five out of the ten top most powerful computers in the world.
IBM is currently developing a successor to the Blue Gene®/L system, named Blue Gene®/P. Blue Gene®/P is expected to be the first computer system to operate at a sustained 1 petaflops (1 quadrillion floating-point operations per second). Like the Blue Gene®/L system, the Blue Gene®/P system is scalable allowing for configurations to include different number of racks.
In addition to the Blue Gene® architecture developed by IBM, other highly parallel computer systems have been (and are being) developed. For example, a Beowulf cluster may be built from a collection of commodity off-the-shelf personal computers. In a Beowulf cluster, individual systems are connected using local area network technology (e.g., Ethernet) and system software is used to execute programs written for parallel processing on the cluster of individual systems. Another approach to parallel computing includes large distributed or grid-type computing systems which pool the computing power of many individual systems spread out at a data center.